Characterisation of spatio-temporal trend in temperature extremes for environmental decision making in Bangladesh
Why this work is in the frame
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Bibliographic record
Abstract
Spatial and sequential variability of extreme temperature events enthral the scientific community owing to their significant impact on global climate change. This study analysed trends in monthly data of temperature extremes of 23 meteorological stations of Bangladesh using Mann-Kendall test. Most of the stations showed significant increasing trend for both temperature extremes on monthly and annual scales. Most of the change points were detected during the last four decades and showed an upward trend. The results obtained from Sen's estimator vouchsafed that magnitudes of trend ranged from 0.007°C to 0.034°C per year and 0.014°C to 0.049°C per year for minimum and maximum temperature, respectively. The upward trend in both extreme temperatures pointed to global warming. The maximum number of significant trends was observed in monsoon and post-monsoon seasons for average maximum temperature. The upward trend in the monsoon and post-monsoon season may cause the drought and late winter in Bangladesh.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it